Cell-cell interaction analysis: Zhang et al.
Yingxin Lin
Last compiled on 22 December, 2020
1 Data and package
library(scran)
library(SingleCellExperiment)
library(scater)
library(scattermore)
library(moon)
library(ggplot2)
library(ggthemes)
library(ggpubr)
library(reshape2)
library(dplyr)
library(stringr)
library(pheatmap)
library(CellChat)
library(gridExtra)
library(RColorBrewer)meta_zhang <- readRDS("results/zhang_results/meta_zhang.rds")
coldata_zhang <- readRDS("results/zhang_results/coldata_zhang.rds")severity_color <- c("#2ca02c", "#FFD92F", "#7570B3", "#1f77b4")
names(severity_color) <- c("Healthy", "Moderate", "Severe", "Conv")# CCI results (Cellchat)
cellchat_res_list <- readRDS("results/zhang_results/zhang_cellchat_res_list.rds")rankNet_byCellType <- function(object, slot.name = "netP",
x.rotation = 90, title = NULL, color.use = NULL,
bar.w = 0.75, font.size = 8)
{
object1 <- methods::slot(object, slot.name)
prob1 = object1$prob
df <- melt(apply(prob1, 3, function(x) {
df <- melt(x)
colnames(df) <- c("Ligand", "Receptor", "value")
df
}))
df <- df[, c("Ligand", "Receptor", "L1", "value")]
colnames(df)[3] <- "Pathway"
return(df)
}2 Overall pattern analysis
rankNet_byCellType_list <- lapply(cellchat_res_list, rankNet_byCellType)
rankNet_byCellType_list <- melt(rankNet_byCellType_list)
rankNet_byCellType_list$Ligand_group <- unlist(lapply(strsplit(as.character(rankNet_byCellType_list$Ligand),
"_"), "[[", 1))
rankNet_byCellType_list$Receptor_group <- unlist(lapply(strsplit(as.character(rankNet_byCellType_list$Receptor),
"_"), "[[", 1))
saveRDS(rankNet_byCellType_list, file = "results/zhang_results/rankNet_byCellType_list_zhang.rds")rankNet_byGroup_agg <- aggregate(rankNet_byCellType_list$value,
list(rankNet_byCellType_list$Ligand_group,
rankNet_byCellType_list$Receptor_group,
rankNet_byCellType_list$L1,
rankNet_byCellType_list$Pathway),
sum)
colnames(rankNet_byGroup_agg) <- c("Ligand_group",
"Receptor_group",
"sample",
"Pathway",
"value")
features <- paste(rankNet_byGroup_agg$Ligand_group,
rankNet_byGroup_agg$Receptor_group,
rankNet_byGroup_agg$Pathway, sep = "_")
rankNet_byGroup_agg$features <- featuresrankNet_byGroup_agg_all <- dcast2(rankNet_byGroup_agg,
features ~ sample,
fun.aggregate = sum, value.var = "value")
rankNet_byGroup_agg_all <- rankNet_byGroup_agg_all[rowSums(rankNet_byGroup_agg_all) > 0, ]
rankNet_byGroup_agg_all <- rankNet_byGroup_agg_all[rowSums(rankNet_byGroup_agg_all!=0) > 2, ]2.1 Feature selction: kruskal test
kruskal_pvalue <- list()
for (i in 1:nrow(rankNet_byGroup_agg_all)) {
if (i %% 100 == 0) print(i)
kruskal_res <- try(kruskal.test(unlist(rankNet_byGroup_agg_all[i,]) ~ meta_zhang[colnames(rankNet_byGroup_agg_all), ]$Condition), silent = TRUE)
kruskal_pvalue[[i]] <- try(kruskal_res$p.value, silent = TRUE)
}## [1] 100
## [1] 200
## [1] 300
## [1] 400
## [1] 500
## [1] 600
## [1] 700
## [1] 800
## [1] 900
## [1] 1000
## [1] 1100
## [1] 1200
## [1] 1300
## [1] 1400
## [1] 1500
## [1] 1600
## [1] 1700
## [1] 1800
## [1] 1900
## [1] 2000
## [1] 2100
## [1] 2200
## [1] 2300
## [1] 2400
## [1] 2500
## [1] 2600
## [1] 2700
## [1] 2800
## [1] 2900
## [1] 3000
## [1] 3100
## [1] 3200
## [1] 3300
## [1] 3400
## [1] 3500
## [1] 3600
## [1] 3700
## [1] 3800kruskal_pvalue <- lapply(kruskal_pvalue, function(x) {
if (class(x) == "try-error") {
x <- NULL
}
x
})
names(kruskal_pvalue) <- rownames(rankNet_byGroup_agg_all)
kruskal_pvalue <- unlist(kruskal_pvalue)
kruskal_pvalue <- p.adjust(kruskal_pvalue, method = "BH")
sort(kruskal_pvalue)[1:20]## B_CD16 Monocyte_BAFF B_CD16 Monocyte_BTLA B_CD16 Monocyte_CD22
## 0.09063109 0.09063109 0.09063109
## B_CD16 Monocyte_CD48 B_CD16 Monocyte_CSF B_CD16 Monocyte_IL16
## 0.09063109 0.09063109 0.09063109
## B_CD16 Monocyte_LIGHT B_CD16 Monocyte_LT B_CD16 Monocyte_MHC-II
## 0.09063109 0.09063109 0.09063109
## B_CD16 Monocyte_MIF B_CD16 Monocyte_NOTCH B_CD16 Monocyte_SEMA4
## 0.09063109 0.09063109 0.09063109
## B_CD4m T_CD45 B_CD4m T_CDH1 B_CD4n T_CD45
## 0.09063109 0.09063109 0.09063109
## B_CD8m T_BTLA B_CD8m T_CD45 B_CD8m T_LT
## 0.09063109 0.09063109 0.09063109
## B_IgA PB_CD23 B_NK_BTLA
## 0.09063109 0.09063109saveRDS(kruskal_pvalue, file = "results/Zhang_results/CCI_kruskal_pvalue_condition_zhang.rds")2.2 PCA
pca_patient <- prcomp(t(-1/log(rankNet_byGroup_agg_all[names(sort(kruskal_pvalue))[1:2000],])), scale. = TRUE, center = TRUE)
library(ggrepel)
pca1 <- ggplot(data.frame(pca_patient$x), aes(x = pca_patient$x[, 1],
y = pca_patient$x[, 2],
color = meta_zhang[rownames(pca_patient$x),]$Condition)) +
geom_point(size = 4, alpha = 0.8) +
# geom_text_repel(aes(label = rownames(pca_patient$x))) +
theme_yx() +
theme(aspect.ratio = 1) +
scale_color_manual(values = severity_color) +
xlab("PCA1") +
ylab("PCA2") +
labs(color = "")
pca2 <- ggplot(data.frame(pca_patient$x), aes(x = pca_patient$x[, 1],
y = pca_patient$x[, 3],
color = meta_zhang[rownames(pca_patient$x),]$Condition)) +
geom_point(size = 3, alpha = 0.8) +
# geom_text_repel(aes(label = rownames(pca_patient$x))) +
theme_yx() +
theme(aspect.ratio = 1) +
scale_color_manual(values = severity_color) +
xlab("PCA1") +
ylab("PCA3") +
labs(color = "")
pca3 <- ggplot(data.frame(pca_patient$x), aes(x = pca_patient$x[, 2],
y = pca_patient$x[, 3],
color = meta_zhang[rownames(pca_patient$x),]$Condition)) +
geom_point(size = 3, alpha = 0.8) +
# geom_text_repel(aes(label = rownames(pca_patient$x))) +
theme_yx() +
theme(aspect.ratio = 1) +
scale_color_manual(values = severity_color) +
xlab("PCA2") +
ylab("PCA3") +
labs(color = "")
ggarrange(pca1, pca2, pca3, align = "hv",
common.legend = TRUE, ncol = 2, nrow = 2)
2.3 Aggregation by samples
aff_mat_bySample <- lapply(split(rankNet_byGroup_agg, rankNet_byGroup_agg$sample),
function(x) dcast2(x, Ligand_group~Receptor_group,
fun.aggregate = mean, value.var = "value"))
all_cellTypes <- sort(unique(unlist(lapply(aff_mat_bySample, dimnames))))
aff_mat_bySample <- lapply(aff_mat_bySample, function(x) {
mat <- matrix(0, ncol = length(all_cellTypes), nrow = length(all_cellTypes))
colnames(mat) <- rownames(mat) <- all_cellTypes
mat[rownames(x), colnames(x)] <- as.matrix(x)
mat
})
selected_cellTypes <- all_cellTypes[!all_cellTypes %in% c("intermediate", "unassigned",
"RBC")]
aff_mat_bySample <- lapply(aff_mat_bySample, function(x) {
x <- x[selected_cellTypes, selected_cellTypes]
x
})
aff_mat_bySample <- lapply(aff_mat_bySample, function(x) {
(x - min(x))/(max(x) - min(x))
})
p <- lapply(1:length(aff_mat_bySample), function(i) {
pheatmap(aff_mat_bySample[[i]],
cluster_cols = FALSE,
cluster_rows = FALSE,
main = names(aff_mat_bySample)[i],
color = colorRampPalette(c("white",
brewer.pal(n = 7,
name = "Reds")))(100))
})
pdf("figures/zhangEtAl/cellchat_CCI_network_sample_byCellType.pdf",
width = 20, height = 20)
do.call(grid.arrange, list(grobs = lapply(p, function(x) x$gtable), ncol = 5))
dev.off()## quartz_off_screen
## 2# severe_patients <- rownames(meta_zhang)[meta_zhang$Condition == "Severe"]
aff_mat_severe <- Reduce("+", aff_mat_bySample[names(aff_mat_bySample) %in% severe_patients])/length(severe_patients)
aff_mat_severe <- aff_mat_severe[selected_cellTypes, selected_cellTypes]
moderate_patients <- rownames(meta_zhang)[meta_zhang$Condition == "Moderate"]
aff_mat_moderate <- Reduce("+", aff_mat_bySample[names(aff_mat_bySample) %in% moderate_patients])/length(moderate_patients)
aff_mat_moderate <- aff_mat_moderate[selected_cellTypes, selected_cellTypes]
control_patients <- rownames(meta_zhang)[meta_zhang$Condition == "Healthy"]
aff_mat_control <- Reduce("+", aff_mat_bySample[names(aff_mat_bySample) %in% control_patients])/length(control_patients)
aff_mat_control <- aff_mat_control[selected_cellTypes, selected_cellTypes]
Conv_patients <- rownames(meta_zhang)[meta_zhang$Condition == "Conv"]
aff_mat_Conv <- Reduce("+", aff_mat_bySample[names(aff_mat_bySample) %in% Conv_patients])/length(Conv_patients)
aff_mat_Conv <- aff_mat_Conv[selected_cellTypes, selected_cellTypes]
p_severe <- pheatmap(aff_mat_severe, cluster_cols = FALSE,
cluster_rows = FALSE,
main = "severe (average across samples)",
color = colorRampPalette(c("white",
brewer.pal(n = 7,
name = "Reds")))(100),
breaks = seq(0, max(aff_mat_severe), max(aff_mat_severe)/100))
library(RColorBrewer)
p_moderate <- pheatmap(aff_mat_moderate,
cluster_cols = FALSE,
cluster_rows = FALSE,
main = "moderate (average across samples)",
color = colorRampPalette(c("white",
brewer.pal(n = 7,
name = "Reds")))(100),
breaks = seq(0, max(aff_mat_moderate), max(aff_mat_moderate)/100))
p_control <- pheatmap(aff_mat_control,
cluster_cols = FALSE,
cluster_rows = FALSE,
main = "control (average across samples)",
color = colorRampPalette(c("white",
brewer.pal(n = 7,
name = "Reds")))(100),
breaks = seq(0, max(aff_mat_control), max(aff_mat_control)/100))
p_Conv <- pheatmap(aff_mat_Conv,
cluster_cols = FALSE,
cluster_rows = FALSE,
main = "convalescence (average across samples)",
color = colorRampPalette(c("white",
brewer.pal(n = 7,
name = "Reds")))(100),
breaks = seq(0, max(aff_mat_Conv), max(aff_mat_Conv)/100))
pdf("figures/zhangEtAl/cellchat_CCI_network_byCondition_noScale.pdf",
width = 8, height = 8)
do.call(grid.arrange, list(grobs = list(p_control$gtable,
p_moderate$gtable,
p_severe$gtable,
p_Conv$gtable), ncol = 2))
dev.off()## quartz_off_screen
## 2aff_mat_diff <- aff_mat_severe - aff_mat_moderate
keep <- intersect(names(which(colSums(aff_mat_diff) != 0)),
names(which(rowSums(aff_mat_diff) != 0)))
pheatmap(aff_mat_diff,
cluster_cols = FALSE,
cluster_rows = FALSE,
color = colorRampPalette(c("blue", "white", "red"))(100),
breaks = c(seq(min(aff_mat_diff), 0, (0 - min(aff_mat_diff))/50),
seq(0.01, max(aff_mat_diff), (max(aff_mat_diff))/50)),
main = "server - moderate (zhang et al.)",
#file = "figures/zhangEtAl/cellchat_CCI_network_byCondition_diff_severe.pdf",
width = 8,
height = 7)
aff_mat_diff <- aff_mat_Conv - aff_mat_moderate
keep <- intersect(names(which(colSums(aff_mat_diff) != 0)),
names(which(rowSums(aff_mat_diff) != 0)))
pheatmap(aff_mat_diff,
cluster_cols = FALSE,
cluster_rows = FALSE,
color = colorRampPalette(c("blue", "white", "red"))(100),
breaks = c(seq(min(aff_mat_diff), 0, (0 - min(aff_mat_diff))/50),
seq(0.01, max(aff_mat_diff), (max(aff_mat_diff))/50)),
main = "convalescence - moderate (zhang et al.)",
#file = "figures/zhangEtAl/cellchat_CCI_network_byCondition_diff_conv.pdf",
width = 8,
height = 7)
aff_mat_diff <- aff_mat_severe - aff_mat_control
keep <- intersect(names(which(colSums(aff_mat_diff) != 0)),
names(which(rowSums(aff_mat_diff) != 0)))
pheatmap(aff_mat_diff,
cluster_cols = FALSE,
cluster_rows = FALSE,
color = colorRampPalette(c("blue", "white", "red"))(100),
breaks = c(seq(min(aff_mat_diff), 0, (0 - min(aff_mat_diff))/50),
seq(0.01, max(aff_mat_diff), (max(aff_mat_diff))/50)),
main = "server - control (zhang et al.)",
#file = "figures/zhangEtAl/cellchat_CCI_network_byCondition_diff_severe_vs_control.pdf",
width = 8,
height = 7)
aff_mat_diff <- aff_mat_moderate - aff_mat_control
pheatmap(aff_mat_diff,
cluster_cols = FALSE,
cluster_rows = FALSE,
color = colorRampPalette(c("blue", "white", "red"))(100),
breaks = c(seq(min(aff_mat_diff), 0, (0 - min(aff_mat_diff))/50),
seq(0.01, max(aff_mat_diff), (max(aff_mat_diff))/50)),
main = "moderate - control (zhang et al.)",
#file = "figures/zhangEtAl/cellchat_CCI_network_byCondition_diff_moderate_vs_control.pdf",
width = 8,
height = 7)
cellType_col <- readRDS("results/pbmc_cellType_col.rds")mat <- aff_mat_control
#mat <- as.matrix(mat)
#mat <- t(Matrix::tril(mat)) + Matrix::triu(mat)
#mat <- mat + t(mat)
cci_control <- melt(as.matrix(mat))
colnames(cci_control) <- c("Ligand", "Receptor", "n")
library(igraph)
library(ggraph)
g <- graph_from_data_frame(data.frame(cci_control))
E(g)$weights <- ifelse(cci_control$n == 0,
1e-10, abs(cci_control$n))
#E(g)$sign <- ifelse(sign(cci_control$n) == 1, "#d62728", "#1f77b4")
V(g)$color <- cellType_col[V(g)$name]
# pdf("figures/ZhangEtAl/cellchat_CCI_network_byCondition_Control_network.pdf",
# width = 8,
# height = 6)
plot(g,
vertex.size = 20,
vertex.color = V(g)$color,
vertex.label.color = "black",
vertex.label.cex = 1,
edge.width = E(g)$weights * 20,
edge.arrow.size = log(1/E(g)$weights)/50,
#edge.arrow.size = 0.01,
#edge.color = E(g)$sign,
edge.curved = 0.3,
layout = layout_in_circle,
main = "Control")
## [1] 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3
## [19] 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3
## [37] 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3
## [55] 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3
## [73] 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3
## [91] 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3
## [109] 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3
## [127] 0.3 0.3 0.3 0.3 0.3 0.3# dev.off()mat <- aff_mat_moderate
#mat <- as.matrix(mat)
#mat <- t(Matrix::tril(mat)) + Matrix::triu(mat)
#mat <- mat + t(mat)
cci_moderate <- melt(as.matrix(mat))
colnames(cci_moderate) <- c("Ligand", "Receptor", "n")
library(igraph)
library(ggraph)
g <- graph_from_data_frame(data.frame(cci_moderate))
E(g)$weights <- ifelse(cci_moderate$n == 0,
1e-10, abs(cci_moderate$n))
#E(g)$sign <- ifelse(sign(cci_moderate$n) == 1, "#d62728", "#1f77b4")
g <- delete_edges(g, E(g)[cci_moderate$n == 0])
V(g)$color <- cellType_col[V(g)$name]
# pdf("figures/ZhangEtAl/cellchat_CCI_network_byCondition_moderate_network.pdf",
# width = 8,
# height = 6)
plot(g,
vertex.size = 20,
vertex.color = V(g)$color,
vertex.label.color = "black",
vertex.label.cex = 1,
edge.width = E(g)$weights * 20,
edge.arrow.size = log(1/E(g)$weights)/50,
#edge.arrow.size = 0.01,
#edge.color = E(g)$sign,
edge.curved = 0.3,
layout = layout_in_circle,
main = "moderate")
## [1] 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3
## [20] 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3
## [39] 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3
## [58] 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3# dev.off()mat <- aff_mat_severe
cci_severe <- melt(as.matrix(mat))
colnames(cci_severe) <- c("Ligand", "Receptor", "n")
library(igraph)
library(ggraph)
g <- graph_from_data_frame(data.frame(cci_severe))
E(g)$weights <- ifelse(cci_severe$n == 0,
1e-10, abs(cci_severe$n))
g <- delete_edges(g, E(g)[cci_severe$n == 0])
V(g)$color <- cellType_col[V(g)$name]
# pdf("figures/ZhangEtAl/cellchat_CCI_network_byCondition_severe_network.pdf",
# width = 8,
# height = 6)
plot(g,
vertex.size = 20,
vertex.color = V(g)$color,
vertex.label.color = "black",
vertex.label.cex = 1,
edge.width = E(g)$weights * 20,
edge.arrow.size = log(1/E(g)$weights)/50,
#edge.arrow.size = 0.01,
#edge.color = E(g)$sign,
edge.curved = 0.3,
layout = layout_in_circle,
main = "severe")
## [1] 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3
## [19] 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3
## [37] 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3
## [55] 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3
## [73] 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3
## [91] 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3
## [109] 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3# dev.off()3 Session Info
sessionInfo()## R version 4.0.2 RC (2020-06-20 r78727)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Catalina 10.15.7
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_AU.UTF-8/en_AU.UTF-8/en_AU.UTF-8/C/en_AU.UTF-8/en_AU.UTF-8
##
## attached base packages:
## [1] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] ggraph_2.0.3 igraph_1.1.0
## [3] ggrepel_0.8.2 RColorBrewer_1.1-2
## [5] gridExtra_2.3 CellChat_0.0.1
## [7] bigmemory_4.5.36 pheatmap_1.0.12
## [9] stringr_1.4.0 dplyr_1.0.2
## [11] reshape2_1.4.4 ggpubr_0.3.0
## [13] ggthemes_4.2.0 moon_0.1.0
## [15] scattermore_0.6 scater_1.16.1
## [17] ggplot2_3.3.2 scran_1.16.0
## [19] SingleCellExperiment_1.10.1 SummarizedExperiment_1.18.1
## [21] DelayedArray_0.14.0 matrixStats_0.56.0
## [23] Biobase_2.48.0 GenomicRanges_1.40.0
## [25] GenomeInfoDb_1.24.2 IRanges_2.22.2
## [27] S4Vectors_0.26.1 BiocGenerics_0.34.0
##
## loaded via a namespace (and not attached):
## [1] readxl_1.3.1 backports_1.1.8
## [3] circlize_0.4.10 systemfonts_0.2.3
## [5] NMF_0.30.1 plyr_1.8.6
## [7] BiocParallel_1.22.0 listenv_0.8.0
## [9] gridBase_0.4-7 digest_0.6.25
## [11] foreach_1.5.0 htmltools_0.5.0
## [13] viridis_0.5.1 ggalluvial_0.12.0
## [15] magrittr_1.5 cluster_2.1.0
## [17] doParallel_1.0.15 openxlsx_4.1.5
## [19] limma_3.44.3 graphlayouts_0.7.0
## [21] sna_2.5 ComplexHeatmap_2.4.2
## [23] globals_0.12.5 svglite_1.2.3.2
## [25] colorspace_1.4-1 haven_2.3.1
## [27] xfun_0.18 crayon_1.3.4
## [29] RCurl_1.98-1.2 jsonlite_1.6.1
## [31] bigmemory.sri_0.1.3 iterators_1.0.12
## [33] glue_1.4.1 polyclip_1.10-0
## [35] registry_0.5-1 gtable_0.3.0
## [37] zlibbioc_1.34.0 XVector_0.28.0
## [39] GetoptLong_1.0.0 car_3.0-8
## [41] BiocSingular_1.4.0 future.apply_1.5.0
## [43] shape_1.4.4 abind_1.4-5
## [45] scales_1.1.1 edgeR_3.30.3
## [47] rngtools_1.5 bibtex_0.4.2.2
## [49] rstatix_0.6.0 Rcpp_1.0.4.6
## [51] viridisLite_0.3.0 xtable_1.8-4
## [53] clue_0.3-57 reticulate_1.16
## [55] dqrng_0.2.1 foreign_0.8-80
## [57] rsvd_1.0.3 FNN_1.1.3
## [59] ellipsis_0.3.1 farver_2.0.3
## [61] pkgconfig_2.0.3 locfit_1.5-9.4
## [63] labeling_0.3 tidyselect_1.1.0
## [65] rlang_0.4.9 munsell_0.5.0
## [67] cellranger_1.1.0 tools_4.0.2
## [69] generics_0.0.2 statnet.common_4.3.0
## [71] broom_0.7.2 evaluate_0.14
## [73] yaml_2.2.1 knitr_1.30
## [75] tidygraph_1.2.0 zip_2.0.4
## [77] purrr_0.3.4 dendextend_1.13.4
## [79] pbapply_1.4-2 future_1.17.0
## [81] compiler_4.0.2 beeswarm_0.2.3
## [83] curl_4.3 png_0.1-7
## [85] ggsignif_0.6.0 tweenr_1.0.1
## [87] tibble_3.0.4 statmod_1.4.34
## [89] stringi_1.4.6 RSpectra_0.16-0
## [91] gdtools_0.2.2 forcats_0.5.0
## [93] lattice_0.20-41 Matrix_1.2-18
## [95] vctrs_0.3.5 pillar_1.4.4
## [97] lifecycle_0.2.0 GlobalOptions_0.1.2
## [99] BiocNeighbors_1.6.0 data.table_1.12.8
## [101] cowplot_1.0.0 bitops_1.0-6
## [103] irlba_2.3.3 R6_2.4.1
## [105] network_1.16.0 rio_0.5.16
## [107] vipor_0.4.5 codetools_0.2-16
## [109] MASS_7.3-51.6 assertthat_0.2.1
## [111] pkgmaker_0.31.1 rjson_0.2.20
## [113] withr_2.2.0 GenomeInfoDbData_1.2.3
## [115] hms_0.5.3 grid_4.0.2
## [117] coda_0.19-3 tidyr_1.1.2
## [119] rmarkdown_2.4 DelayedMatrixStats_1.10.0
## [121] carData_3.0-4 ggforce_0.3.1
## [123] ggbeeswarm_0.6.0